Table REF  shows the results of CaDDN on the KITTI [1]} test set for BEV detection. Our method outpeforms previous single frame methods by large margins on \(\mathrm {AP}|_{R_{40}}\)  of +2.91%, +1.59%, and +2.22% on the Car class on the Easy, Moderate, and Hard difficulties respectively. Our method outperforms the previous state-of-the art method on the Pedestrian class MonoPair [2]} with margins on \(\mathrm {AP}|_{R_{40}}\)  of +3.73%, +2.37%, and +1.88%. Our method achieves first or second place on the Cyclist class with margins on \(\mathrm {AP}|_{R_{40}}\)  of -1.37%, -1.33%, and +0.18% relative to MonoPSR [3]}.
